Detrimental Starfish Detection on Embedded System: A Case Study of YOLOv5 Deep Learning Algorithm and TensorFlow Lite framework

Quoc Toan Nguyen

doi:10.35784/jcsi.2896

Detrimental Starfish Detection on Embedded System: A Case Study of YOLOv5 Deep Learning Algorithm and TensorFlow Lite framework

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Published: Jun 30, 2022

DOI: https://doi.org/10.35784/jcsi.2896

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DOI

https://doi.org/10.35784/jcsi.2896

Authors

Quoc Toan Nguyen

quoctoann3@gmail.com

Hongik University

Abstract

There is a great range of spectacular coral reefs in the ocean world. Unfortunately, they are in jeopardy, due to an overabundance of one specific starfish called the coral-eating crown-of-thorns starfish (or COTS). This article provides research to deliver innovation in COTS control. Using a deep learning model based on the You Only Look Once version 5 (YOLOv5) deep learning algorithm on an embedded device for COTS detection. It aids professionals in optimizing their time, resources and enhancing efficiency for the preservation of coral reefs all around the world. As a result, the performance over the algorithm was outstanding with Precision: 0.93 - Recall: 0.77 - F1-score: 0.84.

Keywords:

deep learning; computer vision; YOLO; embedded system

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Article Details

Nguyen, Q. T. (2022). Detrimental Starfish Detection on Embedded System: A Case Study of YOLOv5 Deep Learning Algorithm and TensorFlow Lite framework. Journal of Computer Sciences Institute, 23, 105–111. https://doi.org/10.35784/jcsi.2896

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